Changing main file
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icm mario.py
483
icm mario.py
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import gym
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import numpy as np
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import torch
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from torch import nn
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import torch.nn.functional as F
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from torchvision import transforms as T
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from PIL import Image
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import numpy as np
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from pathlib import Path
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from collections import deque
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import random, datetime, os, copy
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from torch.distributions import Categorical
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import collections
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import cv2
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import torch.nn.functional as f
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from models import Actor, Critic, Encoder, InverseModel, ForwardModel
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from mario_env import create_mario_env
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from torch.utils.tensorboard import SummaryWriter
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writer = SummaryWriter()
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# Gym is an OpenAI toolkit for RL
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import gym
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from gym.spaces import Box
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from gym.wrappers import FrameStack
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# NES Emulator for OpenAI Gym
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from nes_py.wrappers import JoypadSpace
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# Super Mario environment for OpenAI Gym
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import gym_super_mario_bros
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from gym_super_mario_bros.actions import RIGHT_ONLY, SIMPLE_MOVEMENT, COMPLEX_MOVEMENT
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Make environment
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env = gym.make('SuperMarioBros-1-1-v0')
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env = create_mario_env(env)
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class SkipFrame(gym.Wrapper):
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def __init__(self, env, skip):
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"""Return only every `skip`-th frame"""
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super().__init__(env)
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self._skip = skip
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# Models
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encoder = Encoder(channels=4, encoded_state_size=256).to(device)
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inverse_model = InverseModel(encoded_state_size=256, action_size=env.action_space.n).to(device)
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forward_model = ForwardModel(encoded_state_size=256, action_size=env.action_space.n).to(device)
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actor = Actor(encoded_state_size=256, action_size=env.action_space.n).to(device)
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critic = Critic(encoded_state_size=256).to(device)
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def step(self, action):
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"""Repeat action, and sum reward"""
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total_reward = 0.0
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for i in range(self._skip):
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# Accumulate reward and repeat the same action
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obs, reward, done, trunk, info = self.env.step(action)
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total_reward += reward
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if done:
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break
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return obs, total_reward, done, trunk, info
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# Optimizers
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actor_optim = torch.optim.Adam(actor.parameters(), lr=0.0001)
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critic_optim = torch.optim.Adam(critic.parameters(), lr=0.001)
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icm_params = list(encoder.parameters()) + list(forward_model.parameters()) + list(inverse_model.parameters())
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icm_optim = torch.optim.Adam(icm_params, lr=0.0001)
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# Loss functions
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ce = nn.CrossEntropyLoss().to(device)
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mse = nn.MSELoss().to(device)
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# Hyperparameters
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beta = 0.2
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alpha = 100
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gamma = 0.99
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lamda = 0.1
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# Training Parameters
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render = False
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num_episodes = 1000
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# Training
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def train():
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t = 0
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for episode in range(num_episodes):
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observation = env.reset()
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total_reward = 0
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done = False
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while not done:
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#env.render()
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state = torch.tensor(observation).to(device).unsqueeze(0) if observation.ndim == 3 else torch.tensor(observation).to(device)
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action_probs = actor(state)
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action = action_probs.sample()
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action_one_hot = F.one_hot(action, num_classes=env.action_space.n).float()
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next_observation, reward, done, info = env.step(action.item())
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next_state = torch.tensor(next_observation).to(device).unsqueeze(0) if next_observation.ndim == 3 else torch.tensor(next_observation).to(device)
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encoded_state = encoder(state)
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next_encoded_state = encoder(next_state)
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predicted_next_state = forward_model(encoded_state, action_one_hot)
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predicted_action = inverse_model(encoded_state, next_encoded_state)
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intrinsic_reward = alpha * mse(predicted_next_state, next_encoded_state.detach())
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extrinsic_reward = torch.tensor(reward).to(device)
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reward = intrinsic_reward + extrinsic_reward
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forward_loss = mse(predicted_next_state, next_encoded_state.detach())
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inverse_loss = ce(action_probs.probs,predicted_action.probs)
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icm_loss = beta * forward_loss + (1-beta) * inverse_loss
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delta = reward + gamma * (critic(next_state)*(1-done)) - critic(state)
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actor_loss = -(action_probs.log_prob(action) +1e-6) * delta
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critic_loss = delta ** 2
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ac_loss = actor_loss + critic_loss
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loss = lamda * ac_loss + icm_loss
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actor_optim.zero_grad()
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critic_optim.zero_grad()
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icm_optim.zero_grad()
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loss.backward()
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actor_optim.step()
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critic_optim.step()
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icm_optim.step()
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observation = next_observation
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total_reward += reward.item()
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t +=1
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writer.add_scalar('Loss/Actor Loss', actor_loss.item(), t)
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writer.add_scalar('Loss/Critic Loss', critic_loss.item(), t)
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writer.add_scalar('Loss/Forward Loss', forward_loss.item(), t)
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writer.add_scalar('Loss/Inverse Loss', inverse_loss.item(), t)
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writer.add_scalar('Reward/Episodic Reward', total_reward, episode)
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if episode % 50 == 0:
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torch.save(actor.state_dict(), 'saved_models/actor.pth')
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torch.save(critic.state_dict(), 'saved_models/critic.pth')
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torch.save(encoder.state_dict(), 'saved_models/encoder.pth')
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torch.save(inverse_model.state_dict(), 'saved_models/inverse_model.pth')
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torch.save(forward_model.state_dict(), 'saved_models/forward_model.pth')
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env.close()
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def test():
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actor.load_state_dict(torch.load('saved_models/actor.pth'))
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critic.load_state_dict(torch.load('saved_models/critic.pth'))
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encoder.load_state_dict(torch.load('saved_models/encoder.pth'))
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inverse_model.load_state_dict(torch.load('saved_models/inverse_model.pth'))
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forward_model.load_state_dict(torch.load('saved_models/forward_model.pth'))
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observation = env.reset()
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while True:
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env.render()
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state = torch.tensor(observation).to(device).unsqueeze(0) if observation.ndim == 3 else torch.tensor(observation).to(device)
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action_probs = actor(state)
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action = action_probs.sample()
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observation, reward, done, info = env.step(action.item())
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if done:
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observation = env.reset()
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if __name__ == '__main__':
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train()
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class GrayScaleObservation(gym.ObservationWrapper):
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def __init__(self, env):
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super().__init__(env)
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obs_shape = self.observation_space.shape[:2]
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self.observation_space = Box(low=0, high=255, shape=obs_shape, dtype=np.uint8)
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def permute_orientation(self, observation):
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# permute [H, W, C] array to [C, H, W] tensor
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observation = np.transpose(observation, (2, 0, 1))
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observation = torch.tensor(observation.copy(), dtype=torch.float)
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return observation
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def observation(self, observation):
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observation = self.permute_orientation(observation)
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transform = T.Grayscale()
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observation = transform(observation)
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return observation
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class ResizeObservation(gym.ObservationWrapper):
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def __init__(self, env, shape):
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super().__init__(env)
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if isinstance(shape, int):
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self.shape = (shape, shape)
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else:
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self.shape = tuple(shape)
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obs_shape = self.shape + self.observation_space.shape[2:]
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self.observation_space = Box(low=0, high=255, shape=obs_shape, dtype=np.uint8)
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def observation(self, observation):
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transforms = T.Compose(
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[T.Resize(self.shape), T.Normalize(0, 255)]
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)
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observation = transforms(observation).squeeze(0)
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return observation
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class MaxAndSkipEnv(gym.Wrapper):
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"""
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Each action of the agent is repeated over skip frames
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return only every `skip`-th frame
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"""
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def __init__(self, env=None, skip=4):
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super(MaxAndSkipEnv, self).__init__(env)
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# most recent raw observations (for max pooling across time steps)
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self._obs_buffer = collections.deque(maxlen=2)
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self._skip = skip
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def step(self, action):
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total_reward = 0.0
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done = None
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for _ in range(self._skip):
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obs, reward, done, info = self.env.step(action)
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self._obs_buffer.append(obs)
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total_reward += reward
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if done:
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break
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max_frame = np.max(np.stack(self._obs_buffer), axis=0)
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return max_frame, total_reward, done, info
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def reset(self):
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"""Clear past frame buffer and init to first obs"""
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self._obs_buffer.clear()
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obs = self.env.reset()
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self._obs_buffer.append(obs)
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return obs
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class MarioRescale84x84(gym.ObservationWrapper):
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"""
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Downsamples/Rescales each frame to size 84x84 with greyscale
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"""
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def __init__(self, env=None):
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super(MarioRescale84x84, self).__init__(env)
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self.observation_space = gym.spaces.Box(low=0, high=255, shape=(84, 84, 1), dtype=np.uint8)
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def observation(self, obs):
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return MarioRescale84x84.process(obs)
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@staticmethod
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def process(frame):
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if frame.size == 240 * 256 * 3:
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img = np.reshape(frame, [240, 256, 3]).astype(np.float32)
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else:
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assert False, "Unknown resolution."
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# image normalization on RBG
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img = img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 + img[:, :, 2] * 0.114
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resized_screen = cv2.resize(img, (84, 110), interpolation=cv2.INTER_AREA)
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x_t = resized_screen[18:102, :]
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x_t = np.reshape(x_t, [84, 84, 1])
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return x_t.astype(np.uint8)
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class ImageToPyTorch(gym.ObservationWrapper):
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"""
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Each frame is converted to PyTorch tensors
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"""
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def __init__(self, env):
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super(ImageToPyTorch, self).__init__(env)
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old_shape = self.observation_space.shape
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self.observation_space = gym.spaces.Box(low=0.0, high=1.0, shape=(old_shape[-1], old_shape[0], old_shape[1]), dtype=np.float32)
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def observation(self, observation):
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return np.moveaxis(observation, 2, 0)
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class BufferWrapper(gym.ObservationWrapper):
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"""
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Only every k-th frame is collected by the buffer
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"""
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def __init__(self, env, n_steps, dtype=np.float32):
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super(BufferWrapper, self).__init__(env)
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self.dtype = dtype
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old_space = env.observation_space
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self.observation_space = gym.spaces.Box(old_space.low.repeat(n_steps, axis=0),
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old_space.high.repeat(n_steps, axis=0), dtype=dtype)
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def reset(self):
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self.buffer = np.zeros_like(self.observation_space.low, dtype=self.dtype)
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return self.observation(self.env.reset())
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def observation(self, observation):
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self.buffer[:-1] = self.buffer[1:]
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self.buffer[-1] = observation
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return self.buffer
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class PixelNormalization(gym.ObservationWrapper):
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"""
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Normalize pixel values in frame --> 0 to 1
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"""
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def observation(self, obs):
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return np.array(obs).astype(np.float32) / 255.0
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def create_mario_env(env):
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env = MaxAndSkipEnv(env)
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env = MarioRescale84x84(env)
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env = ImageToPyTorch(env)
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env = BufferWrapper(env, 4)
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env = PixelNormalization(env)
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return JoypadSpace(env, COMPLEX_MOVEMENT)
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class ActorCritic(nn.Module):
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def __init__(self, input_size, action_size=2):
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super(ActorCritic, self).__init__()
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self.input_size = input_size
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self.action_size = action_size
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self.feature = nn.Sequential(
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nn.Conv2d(in_channels=self.input_size[0], out_channels=32, kernel_size=8, stride=4),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1),
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nn.LeakyReLU(),
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nn.Flatten(),
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nn.Linear(in_features=7*7*64, out_features=512),
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nn.LeakyReLU(),
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)
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def actor(self,state):
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policy = nn.Sequential(
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nn.Linear(in_features=state.shape[1], out_features=state.shape[1]),
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nn.LeakyReLU(),
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nn.Linear(in_features=state.shape[1], out_features=self.action_size),
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nn.Softmax(dim=-1)
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).to(device)
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return policy(state)
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def critic(self,state):
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value = nn.Sequential(
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nn.Linear(in_features=state.shape[1], out_features=state.shape[1]),
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nn.LeakyReLU(),
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nn.Linear(in_features=state.shape[1], out_features=1)
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).to(device)
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return value(state)
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def forward(self, state):
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if state.dim() == 3:
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state = state.unsqueeze(0)
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state = self.feature(state)
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value = self.critic(state)
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policy = self.actor(state)
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action_probs = Categorical(policy)
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log_action_probs = torch.log(action_probs.probs)
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return value, action_probs, log_action_probs
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class Encoder(nn.Module):
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def __init__(self, input_size, action_size=2):
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super(Encoder, self).__init__()
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self.input_size = input_size[0]
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self.action_size = action_size
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self.feature_encoder = nn.Sequential(
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nn.Conv2d(in_channels=self.input_size, out_channels=32, kernel_size=3, stride=2),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
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nn.LeakyReLU(),
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nn.Flatten(),
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nn.Linear(in_features=32*4*4, out_features=256),
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).to(device)
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def forward(self, state):
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if state.dim() == 3:
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state = state.unsqueeze(0)
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state = self.feature_encoder(state)
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return state
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class InverseModel(nn.Module):
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def __init__(self, input_size, action_size=2):
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super(InverseModel, self).__init__()
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self.input_size = input_size[0]
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self.action_size = action_size
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self.feature_encoder = Encoder(input_size, action_size)
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self.model = nn.Sequential(
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nn.Linear(in_features=256*2, out_features=256),
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nn.LeakyReLU(),
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nn.Linear(in_features=256, out_features=self.action_size),
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nn.Softmax(dim=-1)
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).to(device)
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def forward(self, state, next_state):
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encoded_state, next_encoded_state = torch.unsqueeze(state, dim=0), torch.unsqueeze(next_state, dim=0)
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encoded_state, next_encoded_state = self.feature_encoder(encoded_state), self.feature_encoder(next_encoded_state)
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encoded_states = torch.cat((encoded_state, next_encoded_state), dim=-1)
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actions = Categorical(self.model(encoded_states))
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a = float(np.array(actions.sample().cpu())[0])
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action = torch.FloatTensor([a])
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one_hot_action = f.one_hot(action.to(torch.int64), self.action_size)
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return one_hot_action, encoded_state, next_encoded_state
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class ForwardModel(nn.Module):
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def __init__(self, encoded_state_size, action_size):
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super(ForwardModel, self).__init__()
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self.state_size = encoded_state_size
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self.action_size = action_size
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self.model = nn.Sequential(
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nn.Linear(self.state_size + 1, 256),
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nn.LeakyReLU(),
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nn.Linear(256, encoded_state_size)
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).to(device)
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def forward(self, state, action):
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if state.dim() == 3:
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state = state.unsqueeze(0)
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if action.dim() == 1:
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action = action.unsqueeze(0)
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state = torch.cat((state, action), dim=-1)
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return self.model(state)
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exit()
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class ICM(nn.Module):
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def __init__(self, state_size, action_size, encoded_state_size=256):
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def __init__(self, state_size, action_size, inverse_model, forward_model, encoded_state_size=256):
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super(ICM, self).__init__()
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self.state_size = state_size
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self.action_size = action_size
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self.inverse_model = InverseModel(state_size, action_size)
|
||||
self.forward_model = ForwardModel(encoded_state_size, action_size)
|
||||
|
||||
self.loss = nn.MSELoss().to(device)
|
||||
|
||||
self.feature_encoder = nn.Sequential(
|
||||
@ -346,61 +171,3 @@ class ICM(nn.Module):
|
||||
|
||||
intrinsic_reward = 0.5 * self.loss(predicted_next_state, next_encoded_state.detach())
|
||||
return intrinsic_reward
|
||||
|
||||
#env = gym.make('SuperMarioBros-1-1-v0')
|
||||
#env = GrayScaleObservation(env)
|
||||
#env = ResizeObservation(env, shape=84)
|
||||
#env = FrameStack(env, num_stack=4)
|
||||
env = gym.make('SuperMarioBros-1-1-v0')
|
||||
env = create_mario_env(env)
|
||||
|
||||
ce = nn.CrossEntropyLoss().to(device)
|
||||
mse = nn.MSELoss().to(device)
|
||||
icm = ICM(env.observation_space.shape, env.action_space.n).to(device)
|
||||
ac = ActorCritic(env.observation_space.shape, env.action_space.n).to(device)
|
||||
optimizer = torch.optim.Adam(list(icm.parameters()) + list(ac.parameters()), lr=0.001)
|
||||
done = False
|
||||
t = 0
|
||||
gamma = 0.99
|
||||
for episode in range(1000):
|
||||
observation = env.reset()
|
||||
total_reward = 0
|
||||
t_init = t
|
||||
while not done:
|
||||
#env.render()
|
||||
value, actions, log_action_probs = ac(torch.FloatTensor(np.array(observation)).to(device))
|
||||
action = actions.sample().item()
|
||||
|
||||
next_observation, reward, done, info = env.step(action) # feedback from environment
|
||||
observation_array, next_observation_array = torch.FloatTensor(np.array(observation)).to(device), torch.FloatTensor(np.array(next_observation)).to(device)
|
||||
|
||||
int_reward = icm(observation_array, next_observation_array, action)
|
||||
|
||||
delta = torch.squeeze(int_reward + gamma * (ac(next_observation_array)[0]*(1-int(done))) - ac(observation_array)[0])
|
||||
actor_loss = -log_action_probs[0,action] * int_reward
|
||||
critic_loss = delta**2
|
||||
|
||||
reward = torch.FloatTensor([reward]).to(device)
|
||||
reward = int_reward
|
||||
|
||||
one_hot_action = icm.inverse_model(observation_array, next_observation_array)[0].to(device)
|
||||
inverse_loss = ce(one_hot_action.float(), actions.probs)
|
||||
|
||||
|
||||
loss = actor_loss + critic_loss + inverse_loss
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
writer.add_scalar("loss", loss, t)
|
||||
|
||||
|
||||
observation = next_observation
|
||||
|
||||
total_reward += reward
|
||||
t += 1
|
||||
#print("timestep: ", t, "reward: ", reward, "loss: ", loss)
|
||||
if done:
|
||||
done = False
|
||||
break
|
||||
writer.add_scalar("reward", total_reward/(t-t_init), t)
|
||||
env.close()
|
||||
Loading…
Reference in New Issue
Block a user